1. Technical Field
The invention relates to the classification of biological tissue. More particularly the invention relates to a method of classifying tissue using non-invasive, in-vivo near-infrared measurements.
2. Description of the Prior Art
Within the biomedical field, examination of the structure and state of an individual""s tissue may yield important information about the individualxe2x80x94for example, the presence or absence of disease, age, or the effect of environmental influences. Tissue biopsy has been an extremely important diagnostic procedure for decades. Additionally, tissue studies are often used to segregate individuals into classes based on the structural and chemical properties of their tissue. For example, transgenic mice have come to play an important role in biomedical research because desired phenotypic and genotypic characteristics can be readily induced by the insertion of foreign genes into their genotype to provide an animal model optimized to the study of a specific scientific problem (see E. Wolfe, R. Wanke, xe2x80x9cGrowth hormone over-production in transgenic mice: phenotypic alterations and deduced animal models,xe2x80x9d Welfare of Transgenic Animals, Springer-Verlag, Heidelberg (1996).
It may be difficult to distinguish the transgenic mice from their non-transgenic littermates, and it would be desirable to have a simple, reliable and noninvasive way to do so. The current practice is to sever a portion of each animal""s tail in order to obtain enough of the animal""s genetic material for use in the various chemical analytical techniques used to study the genome directly ( see R. Wanke, E. Wolf, W. Hermanns, S. Folger, T. Buchmxc3xcller, G. Brem, The GH-Transgenic Mouse as an Experimental Model for Growth Research: Clinical and Pathological Studies Hormone Research, vol. 37, pp. 74-87 (1992)). Such a procedure injures and traumatizes the animal. In addition, the biopsy procedure can be awkward and require the participation of several people. The chemical analytical techniques are costly and time-consuming, and obtaining the desired information can require several different laboratory procedures and take several days or even weeks. It is also impossible to obtain completely accurate information about the in-vivo structure and state of a tissue when the sample has been subjected to the insult inherent in the biopsy procedure. It would be a technological breakthrough to be able to assess the state and structure of a tissue in-vivo rapidly without relying on tissue biopsy and chemical analytical techniques.
Near infrared (NIR) spectroscopy is a promising non-invasive technology that bases measurements on the absorbance of low energy NIR light that is transmitted into a subject. The light is focused onto a small area of the skin and propagated through subcutaneous tissue. The reflected or transmitted light that escapes and is detected by a spectrometer provides information about the structural and chemical properties of the tissue it has penetrated. The absorbance of light at each wavelength is a function of the structural and chemical properties of the tissue. However, application of NIR spectroscopy to perform accurate, noninvasive tissue typing is presently limited by the inability of current models to compensate for the complexity and dimensionality introduced by dramatic changes in the optical properties that occur in a samplexe2x80x94the skin and living tissue of a subjectxe2x80x94as a result of chemical, structural, and physiological variations. Tissue layers, each containing a unique heterogeneous particulate distribution affect light absorbance through scattering and absorbance. Chemical components, such as water, protein, fat, and blood analytes, absorb light proportionally to their concentration through unique absorption profiles, or signatures.
The parent application to the current application, U.S. patent application Ser. No. 09/359,191, An Intelligent System for Noninvasive Blood Analyte Prediction, filed Jul. 22, 1999, by S. Malin, T. Ruchti, discloses a method and apparatus for the use of NIR spectral measurements for predicting blood analyte levels that compensates for co-variation of spectrally interfering species, sample heterogeneity, state variations, and structural variations through an intelligent pattern recognition system. The invention herein disclosed provides a non-invasive method of classifying tissue samples according to chemical and structural properties.
The invention disclosed herein provides a non-invasive, in vivo method of tissue classification according to chemical and structural properties that employs NIR spectral measurements. A tissue classification model is developed by taking NIR spectral absorbance measurements from an exemplary population of individuals. The spectral measurements are assessed to identify features of interest most likely to represent variation between tissue types. Statistical and analytical techniques are used to enhance the features of interest and extract those features representing variation within a tissue. A classification routine determines the best model to define classes within the exemplary population based on variation between tissue types, such that the variation within a class is small compared to the variation between classes. A decision rule assigns class membership to individual members of the representative population based on the structural and chemical properties of each individual""s tissue.
The disclosed tissue classification model is applied in a non-invasive, in-vivo tissue classification procedure using NIR spectral measurements to classify individual tissue samples. The classification model defines classes and provides a set of exemplary data that enable the segregation of test subjects into any of the classes previously defined by the classification model.
A preferred embodiment of the invention is disclosed in which samples of transgenic mice are distinguished from non-transgenic samples based on variation in fat composition of the subcutaneous tissue. The disclosed method correctly classified the samples with an accuracy of 90%.